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Personality Constructs Predictions Beyond FFM/Big5: A Digital Phenotyping-Based Exploration.

Maya Hocherman1, Yonathan Mizrachi2, Hila Chalutz-BenGal3,4

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Journal of Personality
|August 5, 2025
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Summary

Digital phenotyping using smartphone data can predict personality traits. This study successfully predicted 37.29% of 59 traits across 16 constructs, showing potential for remote personality research.

Keywords:
experience sampling methods (ESM)machine learning GBTpeople analyticspersonality researchsmartphone digital phenotyping

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Area of Science:

  • Psychology
  • Computer Science
  • Data Science

Background:

  • Digital phenotyping leverages smartphone data for continuous, in situ behavioral data collection, extending traditional Experience Sampling Methods (ESMs).
  • This research moves beyond the Five-Factor Model (FFM)/Big Five personality constructs to encompass a broader range of 59 traits and types from 16 distinct personality theories, including temperament and personal values.

Purpose of the Study:

  • To investigate the efficacy of digital phenotyping via smartphone data in predicting a wide array of personality constructs.
  • To compare hypothesis-testing and machine learning approaches for analyzing digital footprint data in personality research.
  • To explore the potential of digital phenotyping for remote psychological assessment and People Analytics.

Main Methods:

  • Collected digital footprints from 104 participants' smartphones over 7-10 days.
  • Employed both deductive (hypothesis-testing) and inductive (machine learning) analytical methods.
  • Utilized machine learning models including Gradient Boosted Trees, Decision Tree, Random Forest, and Support Vector Machine.

Main Results:

  • Successfully predicted 4 out of 16 personality constructs (25%), including Adult Attachment, FFM/Big5, Distress Tolerance, and Creativity, with prediction accuracy (r) ranging from 0.034 to 0.53.
  • Achieved an overall prediction success rate of 37.29% for 22 out of 59 individual traits and types.
  • Identified Gradient Boosted Trees as the most effective machine learning model, particularly for communication-related data features.

Conclusions:

  • Digital phenotyping of smartphone data demonstrates significant potential for advancing remote personality psychology research.
  • The findings highlight the applicability of digital phenotyping in People Analytics and other interdisciplinary fields.
  • This approach offers a scalable and objective method for understanding individual differences in personality.